Share this comment
Certainly some fair points about human cognition, but they are also a bit tendentious in relation to the question of what machine learning models of language actually do. Firstly, the first two features you ascribe to language are not, strictly speaking, part of language per se (part of the language faculty, that is); more properly, they…
Certainly some fair points about human cognition, but they are also a bit tendentious in relation to the question of what machine learning models of language actually do. Firstly, the first two features you ascribe to language are not, strictly speaking, part of language per se (part of the language faculty, that is); more properly, they specify some of the uses the capacity for language can be put to (they refer to the roles language can play in cognition overall). Secondly, and whilst the third feature you ascribe to language - compositionality (i.e., structure) - does form part of language, machine learning models of language, as you know, only manipulate strings of elements, not linguistic structures, so this particular point is a bit by-the-by, to be honest - it is certainly another way to show that these ML models don't do natural language at all, but when all is said and done it is really not their objective to model human language, whatever practitioners claim (not to mention the other two ideas from linguistics you mention, which are not even approachable from the perspective an ML model).
What you say here seems right. However, as I understand the authors' intent, the article is really targeting what practitioners are saying. Not to shame them, but to inform people outside the field who may be misled. Admittedly, some are misleading themselves, succumbing to the ELIZA effect, as happened with the Google employee claiming their LaMDA system is sentient.